PyKEEN is a Python package for reproducible, facile knowledge graph embeddings.
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graphs
Building a Knowledge Graph for Job Search Using BERT
In this tutorial, we will build a job recommendation and skill discovery script that will take unstructured text as input, and will then output job recommendations and skill suggestions based on entities such as skills, years of experience, diploma, and major.
We will extract entities and relations from job descriptions using the BERT model and we will attempt to build a knowledge graph from skills and years of experience.
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Graph Machine Learning with Python Part 1: Basics, Metrics, and Algorithms
An introduction to networks via key metrics and algorithms on a Football dataset
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Graph Machine Learning with Python Part 2: Random Graphs and Diffusion Models of CryptoPunks Trading
Simulating and modeling the CryptoPunks trading data via a graph
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How to Build a Knowledge Graph with Neo4J and Transformers
How to build a knowledge graph from job descriptions using fine-tuned transformer-based Named Entity Recognition (NER) and spacy’s relation extraction models. The method described here can be used in any different field such as biomedical, finance, healthcare, etc.
Below are the steps we are going to take:
Load our fine-tuned transformer NER and spacy relation extraction model in google colab
Create a Neo4j Sandbox and add our entities and relations
How to Create and Deploy a Simple Sentiment Analysis App via API
FastAPI might be able to help. FastAPI is FastAPI is a web framework for building APIs with Python. We will use FastAPI in this article to build a REST API to service an NLP model which can be queried via GET request and can dole out responses to those queries.
For this example, we will skip the building of our own model, and instead leverage the Pipeline class of the HuggingFace Transformers library.
